Recent advances in convolution neural network (CNN) have fostered the progress in object recognition and semantic segmentation, which in turn has improved the performance of hyperspectral image (HSI) classification. N...Recent advances in convolution neural network (CNN) have fostered the progress in object recognition and semantic segmentation, which in turn has improved the performance of hyperspectral image (HSI) classification. Nevertheless, the difficulty of high dimensional feature extraction and the shortage of small training samples seriously hinder the future development of HSI classification. In this paper, we propose a novel algorithm for HSI classification based on three-dimensional (3D) CNN and a feature pyramid network (FPN), called 3D-FPN. The framework contains a principle component analysis, a feature extraction structure and a logistic regression. Specifically, the FPN built with 3D convolutions not only retains the advantages of 3D convolution to fully extract the spectral-spatial feature maps, but also concentrates on more detailed information and performs multi-scale feature fusion. This method avoids the excessive complexity of the model and is suitable for small sample hyperspectral classification with varying categories and spatial resolutions. In order to test the performance of our proposed 3D-FPN method, rigorous experimental analysis was performed on three public hyperspectral data sets and hyperspectral data of GF-5 satellite. Quantitative and qualitative results indicated that our proposed method attained the best performance among other current state-of-the-art end-to-end deep learning-based methods.展开更多
语义社会网络(Semantic social network,SSN)是一种由信息节点及社会关系构成的复杂网络,也是语义信息时代社会网络技术研究的热点,相较于传统社会网络更具实用价值.其研究内容包含了社会网络的语义分析及社会关系分析,因此,语义社会网...语义社会网络(Semantic social network,SSN)是一种由信息节点及社会关系构成的复杂网络,也是语义信息时代社会网络技术研究的热点,相较于传统社会网络更具实用价值.其研究内容包含了社会网络的语义分析及社会关系分析,因此,语义社会网络的社区挖掘建模具有一定的复杂性.在语义社会网络的社区挖掘研究方面,本文分析了当前基于话题概率模型的语义社区发现方法,并在综述其内容的同时总结了各方法的优缺点,为后续研究提供了理论基础.在语义社会网络社区挖掘结果的评判方面,本文归纳了相关的评价模型,并通过实验分析对比了各模型对拓扑相关性和语义相关性的倾向性.展开更多
In order to realize high precision of environment parameters detection in irrigation applications,a sensor and sensor network(SSN) ontology based data fusion method is proposed.An SSN sub-ontology for soilstate monito...In order to realize high precision of environment parameters detection in irrigation applications,a sensor and sensor network(SSN) ontology based data fusion method is proposed.An SSN sub-ontology for soilstate monitoring is revised,which includes the sensing devices hierarchies and measurement properties selection according to the detection feature interests.As for sensor data processing,a tuning data method by data pool filtering and clustering is adopted,as well as a useful data fusion method for multi-sensor system.The testing results show that both the accuracy and efficiency of the proposed method are higher after related filtering and clustering process,which enables a thorough monitoring for intelligent irrigation systems and can be extended into environment monitoring and control applications.展开更多
This paper proves that a synchronous demultiplexer has the same logic function as a synchronous multiplexer. A new approach is proposed to implement synchronous demultiplexers in high-speed ISDN switching networks. A ...This paper proves that a synchronous demultiplexer has the same logic function as a synchronous multiplexer. A new approach is proposed to implement synchronous demultiplexers in high-speed ISDN switching networks. A synchronous demultiplexer is designed utilizing the same structure as a synchronous shuffle multiplexer. Both the theoretical analysis and experimental results show that for the same capacity, the new method is more tolerant of signal delay variation, so a very high-speed synchronous demultiplexer can be designed with the larger capacity required in large capacity synchronous switching networks.展开更多
基金the National Natural Science Foundation of China(No.51975374)。
文摘Recent advances in convolution neural network (CNN) have fostered the progress in object recognition and semantic segmentation, which in turn has improved the performance of hyperspectral image (HSI) classification. Nevertheless, the difficulty of high dimensional feature extraction and the shortage of small training samples seriously hinder the future development of HSI classification. In this paper, we propose a novel algorithm for HSI classification based on three-dimensional (3D) CNN and a feature pyramid network (FPN), called 3D-FPN. The framework contains a principle component analysis, a feature extraction structure and a logistic regression. Specifically, the FPN built with 3D convolutions not only retains the advantages of 3D convolution to fully extract the spectral-spatial feature maps, but also concentrates on more detailed information and performs multi-scale feature fusion. This method avoids the excessive complexity of the model and is suitable for small sample hyperspectral classification with varying categories and spatial resolutions. In order to test the performance of our proposed 3D-FPN method, rigorous experimental analysis was performed on three public hyperspectral data sets and hyperspectral data of GF-5 satellite. Quantitative and qualitative results indicated that our proposed method attained the best performance among other current state-of-the-art end-to-end deep learning-based methods.
文摘语义社会网络(Semantic social network,SSN)是一种由信息节点及社会关系构成的复杂网络,也是语义信息时代社会网络技术研究的热点,相较于传统社会网络更具实用价值.其研究内容包含了社会网络的语义分析及社会关系分析,因此,语义社会网络的社区挖掘建模具有一定的复杂性.在语义社会网络的社区挖掘研究方面,本文分析了当前基于话题概率模型的语义社区发现方法,并在综述其内容的同时总结了各方法的优缺点,为后续研究提供了理论基础.在语义社会网络社区挖掘结果的评判方面,本文归纳了相关的评价模型,并通过实验分析对比了各模型对拓扑相关性和语义相关性的倾向性.
基金the National Natural Science Foundation of China(No.61100133)the Science Guidance Project of Education Department of Hubei Province(No.B20101104)
文摘In order to realize high precision of environment parameters detection in irrigation applications,a sensor and sensor network(SSN) ontology based data fusion method is proposed.An SSN sub-ontology for soilstate monitoring is revised,which includes the sensing devices hierarchies and measurement properties selection according to the detection feature interests.As for sensor data processing,a tuning data method by data pool filtering and clustering is adopted,as well as a useful data fusion method for multi-sensor system.The testing results show that both the accuracy and efficiency of the proposed method are higher after related filtering and clustering process,which enables a thorough monitoring for intelligent irrigation systems and can be extended into environment monitoring and control applications.
文摘This paper proves that a synchronous demultiplexer has the same logic function as a synchronous multiplexer. A new approach is proposed to implement synchronous demultiplexers in high-speed ISDN switching networks. A synchronous demultiplexer is designed utilizing the same structure as a synchronous shuffle multiplexer. Both the theoretical analysis and experimental results show that for the same capacity, the new method is more tolerant of signal delay variation, so a very high-speed synchronous demultiplexer can be designed with the larger capacity required in large capacity synchronous switching networks.
